COVER: Identifiable Evaluation of Coalition Routing

• URL: http://arxiv.org/abs/2608.28475v1
• Date: Fri, 28 Aug 2026 16:01:07 GMT
• Title: COVER: Identifiable Evaluation of Coalition Routing
• Abstract summary: When a multi-agent system changes its team, it also changes the messages and final answer it produces, so an end-to-end accuracy gap does not by itself identify a routing effect.<n>We introduce method, an evaluation contract that fixes a public information boundary, downstream stack G, and finite legal team family before outcomes are generated.<n>Complete coverage identifies exact finite-benchmark oracle regret conditional on that stack.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When a multi-agent system changes its team, it also changes the messages and final answer it produces, so an end-to-end accuracy gap does not by itself identify a routing effect. We introduce method, an evaluation contract that fixes a public information boundary, downstream stack G, and finite legal team family before outcomes are generated. Complete coverage identifies exact finite-benchmark oracle regret conditional on that stack. For any finite collection of frozen policies, executing the union of their distinct selected teams is the minimal assumption-free support for every pairwise policy contrast, though not for absolute oracle regret. Two controlled tables with source-ID-disjoint splits test the instrument. On MuSiQue-12, a pre-specified privileged positive control improves regret from 0.532 to 0.402; a later public-interface control reaches 0.424 versus 0.554 but is retrospective. On HotpotQA-4, a pre-specified public direct scorer improves regret from 0.313 to 0.110. In fixed-stack Llama execution, verified route regret improves by 0.190, while the raw-answer gain is 0.010 with an interval crossing zero. A five-family ToolSandbox variant-shift validation exhaustively evaluates 16 declared teams on 14 untouched task variants (224/224 valid rows): the declared-family oracle reaches 0.768 safe-evidence completion, while the prospectively frozen router gets 0.637 (regret 0.131), failing the predeclared 0.10 criterion. A later retrospective comparator reaches 0.655, matching all-workers with 4.57 versus 5.00 workers on average. Thus COVER exposes selection headroom without manufacturing a routing win. A crossed-stack diagnostic shows absolute scores depend on G but finds no detectable router-by-finalizer interaction. COVER is an auditable measurement methodology, not a claim of stack-invariant or universal agent-routing superiority.

Related papers

• LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks [3.789882665982407]
  Multi-agent large language model (LLM) systems can improve reasoning by spending more computation.<n>We isolate this decision by running every problem under four protocols while holding the solver fixed within each setting.
  arXiv  Detail & Related papers  (2026-08-14T22:35:37Z)
• QuoteBench: How Matched Scores Can Hide Command-Path Failures [30.480838412827907]
  Execution scores alone cannot distinguish command-generation errors from failures introduced after generation.<n> QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families.<n> Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state rather than treat a matched score as an intrinsic model property.
  arXiv  Detail & Related papers  (2026-08-13T17:57:20Z)
• Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents [29.96375986740068]
  Harness-IF scores operational rules one at a time from execution evidence.<n>AP-Acc scores only rules labeled as opposing unprompted defaults.
  arXiv  Detail & Related papers  (2026-08-12T07:07:57Z)
• What Would Fix This RAG Failure? Auditing Counterfactual Response with Paired Evidence Interventions [0.0]
  We introduce Pair-ID, an offline audit that holds one query, retrieval state, and reader constant, then crosses two operations, adding missing support and deleting verified nonsupport, to measure a same-failure counterfactual response vector.<n>A complete funnel over 19,981 benchmark queries identifies 11,105 eligible Qwen failures, from which a prospectively fixed SHA-256 ordering selects 1,200 before generating any sampled response.<n>Results show that evidence sensitivity occurs at meaningful rates in the hash-selected eligible-failure sample, is only partially predictable from the observed failure, and is conditional on the reader.
  arXiv  Detail & Related papers  (2026-08-09T22:43:27Z)
• Suppression Sticks, Locality Is Fragile: A Closed-Loop Target-and-Control Audit of Task-Vector Negation in VLA Policies [33.432377306905735]
  Task-vector arithmetic offers a closed-form way to modify a model, yet its behavioral locality remains unclear in closed-loop robot control.<n>We present a target-and-control audit of per-skill task-vector subtraction from multitask vision-language-action (VLA) policies.
  arXiv  Detail & Related papers  (2026-08-05T10:59:53Z)
• Stage-Replay Divergence Follows the KV Cache: Fixed-Prefix Precision Controls and Bidirectional Cache Transplantation [51.56484100374058]
  Stage-replay reconstructs intermediate token prefixes and treat fresh-prefill continuation as continuation from the decoder state that originally reached the prefix.<n>A matched 200-item experiment compares retained live cache with one-shot prefill of identical integer tokens and places an exact replica on both sides.
  arXiv  Detail & Related papers  (2026-07-30T16:41:40Z)
• Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion [46.262619407930266]
  We train a multi-label classifier on a curated gold dataset of 1,207 CVEs from expert MITRE Center for Threat-Informed Defense mappings.<n>The resulting model approximately doubles recall@5 compared with a zero-shot embedding-similarity baseline.<n>We then investigate whether LLM-assisted labeling can extend the gold dataset.
  arXiv  Detail & Related papers  (2026-07-28T10:59:04Z)
• Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary [0.0]
  Can a language model read the quality of its ongoing computation?<n>Can an external intervention turn that readout into better outcomes?<n>We test both questions in a frozen 2.6B looped transformer, Ouro-RLTT.
  arXiv  Detail & Related papers  (2026-07-20T22:40:36Z)
• CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks [47.027290803102666]
  We present CoEval, an open framework that supplies a trustworthy, task-specific signal through ensemble self-evaluation.<n>A pool of models rotates through all three roles, teacher, student, and judge, to generate a fresh, contamination-free benchmark.
  arXiv  Detail & Related papers  (2026-06-02T13:41:43Z)
• Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking [22.825786049667602]
  We propose a replication-first paradigm to anchor validity to a single human-rater consensus.<n>We certify the instrument via four properties -- reliability across K runs, cross-instrument replication across architecturally distinct judges, historical-footprint calibration via judges from earlier training cohorts, and pre-registered prediction.<n>We test it on emotional accompaniment by letting the self-evolve data-driven across: the dimensions are not pre-stipulated and the procedure stabilizes to a 9-dimension set.
  arXiv  Detail & Related papers  (2026-05-27T03:41:11Z)
• A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving [0.0]
  Safety evaluations of language models often treat serving configuration as fixed background infrastructure.<n>We synthesize four artifact-backed studies into a paired testing protocol.<n>Standard vLLM reproduces 22/55 label flips on current score-flip candidates, while enabling VLLM_BATCH_INIANT=1 reduces the same test to 0/55 flips.
  arXiv  Detail & Related papers  (2026-05-26T23:22:55Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.